Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Neighborhood Formation and Anomaly Detection in Bipartite Graphs
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Prediction of Information Diffusion Probabilities for Independent Cascade Model
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part III
Social influence analysis in large-scale networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Causality: Models, Reasoning and Inference
Causality: Models, Reasoning and Inference
Learning influence probabilities in social networks
Proceedings of the third ACM international conference on Web search and data mining
Tweet, Tweet, Retweet: Conversational Aspects of Retweeting on Twitter
HICSS '10 Proceedings of the 2010 43rd Hawaii International Conference on System Sciences
Understanding retweeting behaviors in social networks
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
An empirical study on learning to rank of tweets
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Want to be Retweeted? Large Scale Analytics on Factors Impacting Retweet in Twitter Network
SOCIALCOM '10 Proceedings of the 2010 IEEE Second International Conference on Social Computing
Information diffusion and external influence in networks
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Collaborative personalized tweet recommendation
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
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We study an interesting phenomenon of social influence locality in a large microblogging network, which suggests that users' behaviors are mainly influenced by close friends in their ego networks. We provide a formal definition for the notion of social influence locality and develop two instantiation functions based on pairwise influence and structural diversity. The defined influence locality functions have strong predictive power. Without any additional features, we can obtain a F1-score of 71.65% for predicting users' retweet behaviors by training a logistic regression classifier based on the defined functions. Our analysis also reveals several intriguing discoveries. For example, though the probability of a user retweeting a microblog is positively correlated with the number of friends who have retweeted the microblog, it is surprisingly negatively correlated with the number of connected circles that are formed by those friends.